Clinical encounters, such as patient examinations, are often documented in the form of text reports. These reports can be dictated or typed by the clinician, for example a physician or nurse. An example of such a report is the radiology report, which typically contains some elements of patient history (clinical indication and/or reason for study), a description of the imaging procedure that was performed, and the outcome of the radiological investigation (findings and impression).
To speed up text entry, the next word or words may be predicted using autocompletion. This may be done by means of string pattern matching. When the beginning of a word is typed, the completion of that word may be suggested. To this end, an autocompletion algorithm may find one or more words in a dictionary which begin with the same characters as the characters which have just been typed. For example, entering in “pros” can match against strings such as “prostate” or “prostaglandin”. More sophisticated algorithms can match against phrases: for example, “enlarged pros” will match against “enlarged prostate” but not “prostaglandin”, since the first phrase statistically appears much more often. These matches are often presented to the user as a menu of possibilities, such that the correct possibility can be quickly chosen.
The paper entitled “Semantic autocompletion”, by E. Hyvönen and E. Mäkalä, in Proceedings of the first Asia Semantic Web Conference (ASWC 2006), Beijing, Springer-Verlag, New York, Aug. 4-9, 2006, hereinafter: Hyvönen et al., discloses autocompletion based on matching input strings with a list of usable words in a vocabulary. The paper further discloses completing user written text not only into similar words, but into matching ontological concepts whose labels may not be related to the input on the literal level.